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-- Attaching packages ---------------------------------------------------------------- tidyverse 1.3.1 --
√ ggplot2 3.3.5 √ purrr 0.3.4
√ tibble 3.1.6 √ dplyr 1.0.8
√ tidyr 1.2.0 √ stringr 1.4.0
√ readr 2.1.2 √ forcats 0.5.1
-- Conflicts ------------------------------------------------------------------- tidyverse_conflicts() --
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x dplyr::lag() masks stats::lag()
Attaching package: ‘scales’
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print.shiny.tag tools:rstudio
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data.table 1.14.2 using 8 threads (see ?getDTthreads). Latest news: r-datatable.com
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Rows: 166326 Columns: 67
-- Column specification ---------------------------------------------------------------------------------
Delimiter: ","
chr (4): iso_code, continent, location, tests_units
dbl (62): total_cases, new_cases, new_cases_smoothed, total_deaths, new_deaths, new_deaths_smoothed,...
date (1): date
i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 100416 Columns: 6
-- Column specification ---------------------------------------------------------------------------------
Delimiter: ","
chr (2): location, variant
dbl (3): num_sequences, perc_sequences, num_sequences_total
date (1): date
i Use `spec()` to retrieve the full column specification for this data.
i Specify the column types or set `show_col_types = FALSE` to quiet this message.
nrow(covid)
[1] 166326
covid$location %>%
unique()
[1] "Afghanistan" "Africa"
[3] "Albania" "Algeria"
[5] "Andorra" "Angola"
[7] "Anguilla" "Antigua and Barbuda"
[9] "Argentina" "Armenia"
[11] "Aruba" "Asia"
[13] "Australia" "Austria"
[15] "Azerbaijan" "Bahamas"
[17] "Bahrain" "Bangladesh"
[19] "Barbados" "Belarus"
[21] "Belgium" "Belize"
[23] "Benin" "Bermuda"
[25] "Bhutan" "Bolivia"
[27] "Bonaire Sint Eustatius and Saba" "Bosnia and Herzegovina"
[29] "Botswana" "Brazil"
[31] "British Virgin Islands" "Brunei"
[33] "Bulgaria" "Burkina Faso"
[35] "Burundi" "Cambodia"
[37] "Cameroon" "Canada"
[39] "Cape Verde" "Cayman Islands"
[41] "Central African Republic" "Chad"
[43] "Chile" "China"
[45] "Colombia" "Comoros"
[47] "Congo" "Cook Islands"
[49] "Costa Rica" "Cote d'Ivoire"
[51] "Croatia" "Cuba"
[53] "Curacao" "Cyprus"
[55] "Czechia" "Democratic Republic of Congo"
[57] "Denmark" "Djibouti"
[59] "Dominica" "Dominican Republic"
[61] "Ecuador" "Egypt"
[63] "El Salvador" "Equatorial Guinea"
[65] "Eritrea" "Estonia"
[67] "Eswatini" "Ethiopia"
[69] "Europe" "European Union"
[71] "Faeroe Islands" "Falkland Islands"
[73] "Fiji" "Finland"
[75] "France" "French Polynesia"
[77] "Gabon" "Gambia"
[79] "Georgia" "Germany"
[81] "Ghana" "Gibraltar"
[83] "Greece" "Greenland"
[85] "Grenada" "Guatemala"
[87] "Guernsey" "Guinea"
[89] "Guinea-Bissau" "Guyana"
[91] "Haiti" "High income"
[93] "Honduras" "Hong Kong"
[95] "Hungary" "Iceland"
[97] "India" "Indonesia"
[99] "International" "Iran"
[101] "Iraq" "Ireland"
[103] "Isle of Man" "Israel"
[105] "Italy" "Jamaica"
[107] "Japan" "Jersey"
[109] "Jordan" "Kazakhstan"
[111] "Kenya" "Kiribati"
[113] "Kosovo" "Kuwait"
[115] "Kyrgyzstan" "Laos"
[117] "Latvia" "Lebanon"
[119] "Lesotho" "Liberia"
[121] "Libya" "Liechtenstein"
[123] "Lithuania" "Low income"
[125] "Lower middle income" "Luxembourg"
[127] "Macao" "Madagascar"
[129] "Malawi" "Malaysia"
[131] "Maldives" "Mali"
[133] "Malta" "Marshall Islands"
[135] "Mauritania" "Mauritius"
[137] "Mexico" "Micronesia (country)"
[139] "Moldova" "Monaco"
[141] "Mongolia" "Montenegro"
[143] "Montserrat" "Morocco"
[145] "Mozambique" "Myanmar"
[147] "Namibia" "Nauru"
[149] "Nepal" "Netherlands"
[151] "New Caledonia" "New Zealand"
[153] "Nicaragua" "Niger"
[155] "Nigeria" "Niue"
[157] "North America" "North Macedonia"
[159] "Northern Cyprus" "Norway"
[161] "Oceania" "Oman"
[163] "Pakistan" "Palau"
[165] "Palestine" "Panama"
[167] "Papua New Guinea" "Paraguay"
[169] "Peru" "Philippines"
[171] "Pitcairn" "Poland"
[173] "Portugal" "Qatar"
[175] "Romania" "Russia"
[177] "Rwanda" "Saint Helena"
[179] "Saint Kitts and Nevis" "Saint Lucia"
[181] "Saint Pierre and Miquelon" "Saint Vincent and the Grenadines"
[183] "Samoa" "San Marino"
[185] "Sao Tome and Principe" "Saudi Arabia"
[187] "Senegal" "Serbia"
[189] "Seychelles" "Sierra Leone"
[191] "Singapore" "Sint Maarten (Dutch part)"
[193] "Slovakia" "Slovenia"
[195] "Solomon Islands" "Somalia"
[197] "South Africa" "South America"
[199] "South Korea" "South Sudan"
[201] "Spain" "Sri Lanka"
[203] "Sudan" "Suriname"
[205] "Sweden" "Switzerland"
[207] "Syria" "Taiwan"
[209] "Tajikistan" "Tanzania"
[211] "Thailand" "Timor"
[213] "Togo" "Tokelau"
[215] "Tonga" "Trinidad and Tobago"
[217] "Tunisia" "Turkey"
[219] "Turkmenistan" "Turks and Caicos Islands"
[221] "Tuvalu" "Uganda"
[223] "Ukraine" "United Arab Emirates"
[225] "United Kingdom" "United States"
[227] "Upper middle income" "Uruguay"
[229] "Uzbekistan" "Vanuatu"
[231] "Vatican" "Venezuela"
[233] "Vietnam" "Wallis and Futuna"
[235] "World" "Yemen"
[237] "Zambia" "Zimbabwe"
covid_NAs <- covid %>%
group_by(location) %>%
summarise_all(funs(sum(is.na(.)))) %>%
pivot_longer(cols = -location, names_to = "Variable", values_to = "NAs") %>%
mutate(Percent = round(NAs / nrow(covid) * 100 ,2)) %>%
arrange(-NAs)
Warning: `funs()` was deprecated in dplyr 0.8.0.
Please use a list of either functions or lambdas:
# Simple named list:
list(mean = mean, median = median)
# Auto named with `tibble::lst()`:
tibble::lst(mean, median)
# Using lambdas
list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
covid_NAs
DT::datatable(
covid_NAs, filter = 'top',
#options = list(
# columnDefs = list(list(targets = 1, searchable = FALSE))
#)
)
covid_NAs %>%
group_by(location) %>%
summarise(total_pct_na = sum(Percent)) %>%
arrange(total_pct_na) %>%
datatable(filter = 'top')
covid %>%
colnames()
[1] "iso_code" "continent"
[3] "location" "date"
[5] "total_cases" "new_cases"
[7] "new_cases_smoothed" "total_deaths"
[9] "new_deaths" "new_deaths_smoothed"
[11] "total_cases_per_million" "new_cases_per_million"
[13] "new_cases_smoothed_per_million" "total_deaths_per_million"
[15] "new_deaths_per_million" "new_deaths_smoothed_per_million"
[17] "reproduction_rate" "icu_patients"
[19] "icu_patients_per_million" "hosp_patients"
[21] "hosp_patients_per_million" "weekly_icu_admissions"
[23] "weekly_icu_admissions_per_million" "weekly_hosp_admissions"
[25] "weekly_hosp_admissions_per_million" "new_tests"
[27] "total_tests" "total_tests_per_thousand"
[29] "new_tests_per_thousand" "new_tests_smoothed"
[31] "new_tests_smoothed_per_thousand" "positive_rate"
[33] "tests_per_case" "tests_units"
[35] "total_vaccinations" "people_vaccinated"
[37] "people_fully_vaccinated" "total_boosters"
[39] "new_vaccinations" "new_vaccinations_smoothed"
[41] "total_vaccinations_per_hundred" "people_vaccinated_per_hundred"
[43] "people_fully_vaccinated_per_hundred" "total_boosters_per_hundred"
[45] "new_vaccinations_smoothed_per_million" "new_people_vaccinated_smoothed"
[47] "new_people_vaccinated_smoothed_per_hundred" "stringency_index"
[49] "population" "population_density"
[51] "median_age" "aged_65_older"
[53] "aged_70_older" "gdp_per_capita"
[55] "extreme_poverty" "cardiovasc_death_rate"
[57] "diabetes_prevalence" "female_smokers"
[59] "male_smokers" "handwashing_facilities"
[61] "hospital_beds_per_thousand" "life_expectancy"
[63] "human_development_index" "excess_mortality_cumulative_absolute"
[65] "excess_mortality_cumulative" "excess_mortality"
[67] "excess_mortality_cumulative_per_million"
head(covid$date)
[1] "2020-02-24" "2020-02-25" "2020-02-26" "2020-02-27" "2020-02-28" "2020-02-29"
summary(variants)
location date variant num_sequences perc_sequences
Length:100416 Min. :2020-05-11 Length:100416 Min. : 0.00 Min. : -0.010
Class :character 1st Qu.:2020-10-26 Class :character 1st Qu.: 0.00 1st Qu.: 0.000
Mode :character Median :2021-03-22 Mode :character Median : 0.00 Median : 0.000
Mean :2021-03-13 Mean : 72.17 Mean : 6.154
3rd Qu.:2021-07-26 3rd Qu.: 0.00 3rd Qu.: 0.000
Max. :2022-01-05 Max. :142280.00 Max. :100.000
num_sequences_total
Min. : 1
1st Qu.: 12
Median : 59
Mean : 1510
3rd Qu.: 394
Max. :146170
variants %>%
filter(location == "United States") %>%
## filter(variant %in% c("Alpha", "Delta", "Omicron")) %>%
ggplot(aes(x = date, y = perc_sequences, color = variant)) +
geom_line(size = 1, alpha = 0.5) +
theme_minimal()

variants %>%
filter(location == "United States", date == max(date)) %>%
arrange(-perc_sequences)
variants$date
[1] "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06"
[8] "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06"
[15] "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06"
[22] "2020-07-06" "2020-07-06" "2020-07-06" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31"
[29] "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31"
[36] "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31"
[43] "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-09-28"
[50] "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28"
[57] "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28"
[64] "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28"
[71] "2020-09-28" "2020-09-28" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12"
[78] "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12"
[85] "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12"
[92] "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-26" "2020-10-26"
[99] "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26"
[106] "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26"
[113] "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26"
[120] "2020-10-26" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07"
[127] "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07"
[134] "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07"
[141] "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-07" "2020-12-21" "2020-12-21" "2020-12-21"
[148] "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21"
[155] "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21"
[162] "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21" "2020-12-21"
[169] "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04"
[176] "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04"
[183] "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-04"
[190] "2021-01-04" "2021-01-04" "2021-01-04" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11"
[197] "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11"
[204] "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11"
[211] "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-11" "2021-01-25"
[218] "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25"
[225] "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25"
[232] "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25" "2021-01-25"
[239] "2021-01-25" "2021-01-25" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08"
[246] "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08"
[253] "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08"
[260] "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-08" "2021-02-22" "2021-02-22"
[267] "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22"
[274] "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22"
[281] "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22" "2021-02-22"
[288] "2021-02-22" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08"
[295] "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08"
[302] "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08"
[309] "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-08" "2021-03-22" "2021-03-22" "2021-03-22"
[316] "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22"
[323] "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22"
[330] "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22" "2021-03-22"
[337] "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05"
[344] "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05"
[351] "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-05"
[358] "2021-04-05" "2021-04-05" "2021-04-05" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19"
[365] "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19"
[372] "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19"
[379] "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-04-19" "2021-05-03"
[386] "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03"
[393] "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03"
[400] "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03" "2021-05-03"
[407] "2021-05-03" "2021-05-03" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17"
[414] "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17"
[421] "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17"
[428] "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-17" "2021-05-31" "2021-05-31"
[435] "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31"
[442] "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31"
[449] "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31" "2021-05-31"
[456] "2021-05-31" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14"
[463] "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14"
[470] "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14"
[477] "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-14" "2021-06-28" "2021-06-28" "2021-06-28"
[484] "2021-06-28" "2021-06-28" "2021-06-28" "2021-06-28" "2021-06-28" "2021-06-28" "2021-06-28"
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[631] "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20"
[638] "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20" "2021-09-20"
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[673] "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11"
[680] "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11"
[687] "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-11"
[694] "2020-05-11" "2020-05-11" "2020-05-11" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25"
[701] "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25"
[708] "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25"
[715] "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-05-25" "2020-06-08"
[722] "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08"
[729] "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08"
[736] "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08" "2020-06-08"
[743] "2020-06-08" "2020-06-08" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22"
[750] "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22"
[757] "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22"
[764] "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-06-22" "2020-07-06" "2020-07-06"
[771] "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06"
[778] "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06"
[785] "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06" "2020-07-06"
[792] "2020-07-06" "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20"
[799] "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20" "2020-07-20"
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[834] "2020-08-03" "2020-08-03" "2020-08-03" "2020-08-03" "2020-08-03" "2020-08-03" "2020-08-03"
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[848] "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17"
[855] "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17" "2020-08-17"
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[869] "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31"
[876] "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31"
[883] "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-08-31" "2020-09-14"
[890] "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14"
[897] "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14"
[904] "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14" "2020-09-14"
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[918] "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28"
[925] "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28"
[932] "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-09-28" "2020-10-12" "2020-10-12"
[939] "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12"
[946] "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12"
[953] "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12" "2020-10-12"
[960] "2020-10-12" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26"
[967] "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26"
[974] "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26"
[981] "2020-10-26" "2020-10-26" "2020-10-26" "2020-10-26" "2020-11-09" "2020-11-09" "2020-11-09"
[988] "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09"
[995] "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09" "2020-11-09"
[ reached 'max' / getOption("max.print") -- omitted 99416 entries ]
us <- covid %>%
filter(location == "United States")
us_variants <- variants %>%
filter(location == "United States")
us <- left_join(us, us_variants, by = "date")
variants_plot <- us %>%
ggplot(aes(x = date)) +
geom_line(aes(y = perc_sequences, color = variant), show.legend = FALSE) +
geom_vline(aes(xintercept = ymd(20200706)), color = "black") +
geom_vline(aes(xintercept = ymd(20210517)), color = "black") +
geom_vline(aes(xintercept = ymd(20211004)), color = "black") +
geom_vline(aes(xintercept = ymd(20220105)), color = "black") +
theme_minimal()
cases_plot <- us %>%
ggplot(aes(x = date)) +
geom_line(aes(y = new_cases_per_million), show.legend = FALSE) +
geom_line(aes(y = new_deaths_per_million)) +
geom_vline(aes(xintercept = ymd(20200706)), color = "black") +
geom_vline(aes(xintercept = ymd(20210517)), color = "black") +
geom_vline(aes(xintercept = ymd(20211004)), color = "black") +
geom_vline(aes(xintercept = ymd(20220105)), color = "black") +
theme_minimal()
ggplotly(variants_plot)
Warning: `gather_()` was deprecated in tidyr 1.2.0.
Please use `gather()` instead.
This warning is displayed once every 8 hours.
Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
variants_plot
Warning: Removed 729 row(s) containing missing values (geom_path).

cases_plot
Warning: Removed 1 row(s) containing missing values (geom_path).
Warning: Removed 38 row(s) containing missing values (geom_path).

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